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Record W2153776179 · doi:10.1139/l11-035

Mechanical capabilities and fire endurance of natural rubber latex modified concrete

2011· article· en· W2153776179 on OpenAlexvenueno aff
Mohammad Ismail, Bala Muhammad, Abdirahman Ali Yussuf, Zaiton Abdul Majid, Mohamed A. Ismail

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsnot available
FundersKementerian Pendidikan MalaysiaUniversiti Teknologi Malaysia
KeywordsNatural rubberMaterials scienceComposite materialUltimate tensile strengthCuring (chemistry)Flexural strengthCompressive strengthStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Compressive, indirect tensile, and flexural strengths characteristics of natural rubber latex modified concrete (NRLMC) were studied and reported — Part I. In addition, a comparative investigation between normal concrete (NC) and NRLMC, conducted on fire endurance was also presented — Part II. Normal and modified elements containing latex–water ratios within 0–10% were prepared and cured in accordance with the Japan Industrial Standard. Fire endurance capacity was assessed at five temperature levels; ambient temperature (27 °C), 150, 300, 500, and 800 °C. However, elevated heating (800–1300 °C) was further applied for the purpose of monitoring behavior at the verge of disintegration. Specimens were allowed to cool in the oven prior to compressive strength tests until room temperature is attained. Test results indicate that significant mechanical capabilities could be achieved by the inclusion of an appropriate quantity of latex concentrate and proper selection of befitting curing regime. Interestingly, the superiority of NRLMC over NC is maintained even under fire, up to the critical limit of latex-film capability performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.163
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2011
Admission routes1
Has abstractyes

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